Claude Haiku 5.5: should you switch models for support and repetitive tasks?

The Anthropic announcement shows $0.10 per million input tokens and $0.50 per million output tokens up to 100,000 prompt tokens. Beyond that, the amounts shown are $0.50 and $2.50. The rate therefore also depends on the volume transmitted in a request.

Prices are in US dollars, as indicated by the pricing documentation. They concern the model. The support workflow can add a platform, document research, and calls to other tools. The AI agent budget guide presents a method to include preparation, operation, and corrections.

In a fictional example, the model prepares a response to a delivery question based on an approved policy. If the order number is missing, it asks the planned question. If the request concerns an exceptional refund, the file goes to an employee. This separation must be defined with the service manager.

Prepare a set of representative requests and expected responses. Keep the same documents and tools to compare models. Have the results reviewed without revealing the model used when possible. Count the correct responses, fabricated information, and necessary escalations.

The model must be able to recognize missing information. A convincing response that announces an unauthorized refund may cost more than several calls to a more expensive model. The control of a financial operation must remain in the tool that executes it, with the validation provided by the company.

For a simulation, let's assume 1,000 requests with 900 accepted responses in one version of the system and 800 in another. Then divide the expenses of each version by its number of accepted responses. Next, add human review time. Both versions may use similar token budgets and leave different workloads for the team.

Also monitor response time and the size of the context sent. An entire support policy may be unnecessary when a targeted passage is sufficient. Reducing the data transmitted should retain the information necessary for a good response.

The first AI workflow with n8n approach provides starting controls for a pilot. The AI for customer service service allows scoping processed requests, escalations, and useful measurements before changing the model used in production.